FPGA Roofline modeling and its Application to Visual SLAM.

The Roofline model has been proposed to visually associate application performance against the computational and bandwidth capabilities of the underlying platform. Since FPGAs lack fixed operation units, modifications in the original CPU-based Roofline model should be made. In this paper, we propose a new application-centric approach to construct the FPGA Roofline model extending previous work and encompassing resource and latency constraints to provide a more fitting ceiling. Moreover, we generalize our model to accommodate platforms with multiple accelerators whose execution footprint may be strongly input-dependent due to conditionals and complex loop structures. We evaluate our model and compare it with previous models on KinectFusion, a complex, multi-kernel algorithm for visual Simultaneous Localization and Mapping (vSLAM) used for autonomous agent navigation. Our work makes it feasible to deploy Roofline analysis on a wider range of MPSoC-based FPGAs that consist of more complex HW/ SW components and not just single accelerators.